Study on constructing method of classifying decision tree based on variable precision rough set
Bian Yu-qian · Systems engineering and electronics · 2008
Considering difficulty of choosing the best attribute and dealing with large-scale data set in constructing classifying decision tree,a new selection criterion called importance measure of attributes' classification(IMAC) and a decision tree constructing algorithm based on VPRS are proposed.The IMAC can describe classification capabilities of attributes comprehensively,and is simpler than traditional information entropy in calculation.In order to control growing up of the decision tree,confidence and support are introduced in algorithm;it can not only reduce the size of decision tree but also enhance the capability of decision tree in processing noise data and incompatible data.The proposed algorithm is tested with five different size and type of data sets in the UCI,the results show that proposed method is more efficient than ID3 algorithm,and equal to the best results of the UCI.